Health data sharing attitudes towards primary and secondary use of data: a systematic review

Summary Background To receive the best care, people share their health data (HD) with their health practitioners (known as sharing HD for primary purposes). However, during the past two decades, sharing for other (i.e., secondary) purposes has become of great importance in numerous fields, including public health, personalized medicine, research, and development. We aimed to conduct the first comprehensive overview of all studies that investigated people's HD sharing attitudes—along with associated barriers/motivators and significant influencing factors—for all data types and across both primary and secondary uses. Methods We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for relevant studies published in English between database inception and February 28, 2023, using a predefined set of keywords. Studies were included, regardless of their design, if they reported outcomes related to attitudes towards sharing HD. We extracted key data from the included studies, including the type of HD involved and findings related to: HD sharing attitudes (either in general or depending on type of data/user); barriers/motivators/benefits/concerns of the study participants; and sociodemographic and other variables that could impact HD sharing behaviour. The qualitative synthesis was conducted by dividing the studies according to the data type (resulting in five subgroups) as well as the purpose the data sharing was focused on (primary, secondary or both). The Newcastle–Ottawa Scale (NOS) was used to assess the quality of non-randomised studies. This work was registered with PROSPERO, CRD42023413822. Findings Of 2109 studies identified through our search, 116 were included in the qualitative synthesis, yielding a total of 228,501 participants and various types of HD represented: person-generated HD (n = 17 studies and 10,771 participants), personal HD in general (n = 69 studies and 117,054 participants), Biobank data (n = 7 studies and 27,073 participants), genomic data (n = 13 studies and 54,716 participants), and miscellaneous data (n = 10 studies and 18,887 participants). The majority of studies had a moderate level of quality (83 [71.6%] of 116 studies), but varying levels of quality were observed across the included studies. Overall, studies suggest that sharing intentions for primary purposes were observed to be high regardless of data type, and it was higher than sharing intentions for secondary purposes. Sharing for secondary purposes yielded variable findings, where both the highest and the lowest intention rates were observed in the case of studies that explored sharing biobank data (98% and 10%, respectively). Several influencing factors on sharing intentions were identified, such as the type of data recipient, data, consent. Further, concerns related to data sharing that were found to be mutual for all data types included privacy, security, and data access/control, while the perceived benefits included those related to improvements in healthcare. Findings regarding attitudes towards sharing varied significantly across sociodemographic factors and depended on data type and type of use. In most cases, these findings were derived from single studies and therefore warrant confirmations from additional studies. Interpretation Sharing health data is a complex issue that is influenced by various factors (the type of health data, the intended use, the data recipient, among others) and these insights could be used to overcome barriers, address people's concerns, and focus on spreading awareness about the data sharing process and benefits. Funding None.


Introduction
To receive the best care, people readily share their health data (HD) with their health practitioners (which is known as sharing HD for primary purposes).However, during the past two decades, secondary health data (HD) use has experienced an expansion.Secondary purposes can include sharing for both clinical and public health research, publishing national statistics, education, developing algorithms, and others.Secondary HD use largely depends on people's willingness to share their data.This expansion started in 2002 when the World Medical Association's Declaration on Ethical Considerations regarding Health Databases recognized the importance of databases in health research, quality assurance and risk management, 1 which generated demand for using HD for purposes outside of just with a general practitioner.The benefits of HD reuse were further acknowledged in 2006 by the Medical Research Council and Welcome Trust who emphasized the need for data sharing optimization for research purposes. 2So far, there have been several statements brought by eminent organizations such as the Welcome Trust and the Hewlett Foundation, that focused on increasing the availability of the data collected from the research they fund and promoting their use to facilitate public health research. 3Similar statements were published afterwards from the International Committee of Medical Journal Editors 4 and the US Institute of Medicine 5 where the focus was put on the "ethical obligation to share" and proposed that data sharing should be the expected norm.
However, a clarification about what HD refers to and the distinction between primary and secondary uses of HD should be made.According to the General Data Protection Regulation (GDPR), health data is defined as "all data pertaining to the health status of a data subject which reveal information relating to the past, current or future physical or mental health status of the data subject." 6More specifically, HD includes a wide range of data such as those derived from medical examinations, lab tests, genetic data, those gathered via medical devices, mobile apps, etc.When it comes to the purpose

Research in context
Evidence before this study Prior to conducting this study, we performed a search of all published reviews that explored health data sharing behaviour/intentions.The published reviews have so far attempted to investigate specific aspects related to attitudes or willingness to share health data by narrowing their selection criteria and focusing factors such as the type of use, the type of health data, type of study design and more.There was no systematic review that summarized all findings regarding willingness to share HD or information (regardless of the study designs), the corresponding motives and concerns, significant predictors, and to draw conclusions based on sharing for primary and secondary purposes.We aimed to fill this knowledge gap.On February 28, 2023, we used the following search terms ("health data" OR "patient data" OR "medical data" OR "health information" OR "person generated health data" OR "electronic health records" OR "mobile apps" OR "apps" OR "wearable technology" OR "wearable device" OR "big data") AND ("sharing" OR "data sharing" OR "health data sharing" OR "health information exchange") AND ("attitudes" OR "beliefs" OR "perceptions" OR "willingness").This search yielded 2109 articles for screening, aiming to provide a thorough understanding of the current landscape in health data sharing intentions and behaviors.

Added value of this study
This systematic review included 116 studies, divided into five subgroups according to the data type that was investigated to be shared: person-generated HD (collected via sensors, devices, etc., n = 17), personal HD in general (n = 69), biobank data (n = 7), genomic data (n = 13), and the fifth subgroup reported miscellaneous data (n = 10).Overall, this systematic review provided the first comprehensive qualitative summary of findings related to sharing attitudes/behaviours (and associated predictors of this behaviour, and opinions of the study populations that were classified as being concerns/ motivators that were related to sharing behaviour) depending on the data type and the type of use (primary or secondary).

Implications of all the available evidence
This thorough systematic review revealed that people are more open to share their data for the means of receiving care.Although a great number of studies concluded high support for some secondary uses (such as for non-commercial research), the attitude varies greatly depending on the type of data recipient.This review identified that people share the same concerns related to privacy and safety regardless of data type and that they would share their data as they perceive it will help improve healthcare and advance health knowledge.Overcoming barriers, addressing concerns, and spreading awareness about data sharing practices may lead to a more active data-sharing society.
for which HD can be shared, primary use of data is typically performed by the entities that produce or collect these data while providing real-time, direct care to the healthcare consumers, while secondary use of data refers to non-direct care use of health information, such as analysis, research, quality/safety measurement, public health, payment, provider certification or accreditation, education/teaching services, etc. 7 Sharing health data can lead to improved data transparency, better research reproducibility, 8 and increased cost-effectiveness as a result of minimizing the repetition of research work.As a result, science and clinical knowledge can advance and the process of finding effective and safe patient treatments can be accelerated. 8It is noteworthy to point to some of the technological advancements that are reported to be a result of data-sharing initiatives.For example, the sharing of primary data (such as those collected via Health Information Exchange (HIE) for secondary uses were seen to bring advances at the healthcare system level.The United Kingdom National Health Service (NHS) has placed a focus on continuing their digital transformation and their ability to share primary care data with secondary care clinicians.During the period between 2015 and 2019, the NHS evidenced a rapid evolution of interoperable technology that allowed data to easily be shared between primary and secondary care practitioners.According to one study, primary to secondary care data-sharing capabilities were associated with a reduction in the number of patients breaching an Accident & Emergency 4-h decision time threshold in addition to an improved experience for patients who reported improved quality of acute hospital care.This is one piece of evidence that confirms that improvement in data-sharing capacities can bring benefits in the quality and service in healthcare systems. 9Some of the latest technological advancements that are based on sharing health data and their reuse can be seen in the example of digital twins.Digital twins represent a digital model or replication of physical entities-for example, it can be a virtual replica of human organs, tissues, cells that is used for predicting corresponding future scenarios. 10Digital twins are seen to revolutionize healthcare systems by allowing integration of real-time data, advanced analytics, and virtual simulations that can lead to improved patient care, conducting predictive analytics, optimization of clinical operations, and performing training and simulation. 10Additionally, the synergy between artificial intelligence and big data are continuously leveraging novel algorithms that are used in disease prediction, diagnosis, or in predicting therapeutical outcomes, among other applications. 11long with conveying the importance of the use of HD for secondary purposes is the importance of assuring public acceptance towards data sharing practices. 12In one part, this means that secondary health data use should be aligned with the interests of the public (donors of health data), and thus it needs to have public support.To enable efficient secondary use of health data, it is necessary that health data donors understand the aspects surrounding the secondary use of their data and that way enables their support.However, sharing for both primary and secondary purposes come with associated challenges.Empirical evidence shows that people express different intentions when sharing depending on whether they had to enclose their HD with healthcare professionals within the same department, hospital, or some institution outside their hospital chain. 13,146][17] It also largely depends on the level of anonymity of HD, which can be either anonymous, de-identified, reversibly anonymous or identifiable information that are publicly available, and evidence suggests that people express higher support for sharing their HD with higher levels of privacy. 18,19Previous research that has dealt with the issue related to HD sharing, mainly focused on exploring the practitioners' views, while data addressing the public opinion on this topic was scarce. 20However, this has changed during the last decade, as an increasing number of studies that investigated public acceptability towards secondary use of HD have been published. 21everal systematic reviews have summarized the findings from both qualitative and quantitative studies that explored the issues related to the public attitude regarding HD sharing for primary and secondary uses.According to the published evidence, two systematic reviews concluded that the support for HD sharing was widespread, however, with certain conditions, and accompanied by concerns (e.g., privacy, transparency). 21,22Other authors collected evidence about significant sociodemographic predictors and concluded that male and older individuals expressed higher willingness to give their consent for secondary review of their medical data and consequent use for research. 15nother systematic review explored attitudes regarding sharing data for secondary research use by performing a thematic analysis and yielded four key themes: benefits, fears and harms of data sharing, data sharing processes (especially the role of consent), and the participantsresearch relationship. 23The published systematic reviews have so far been designed to explore specific aspects related to attitudes or willingness to share HD, and their selection criteria was narrow and focused on the type of use, the type of data or the type of study design.No systematic review has been aimed at summarizing all findings regarding willingness to share HD or information (regardless of the study designs), the corresponding motives and concerns and the significant predictors, then draw conclusions based on sharing for primary and secondary purposes.
Therefore, this systematic review represents the first comprehensive overview of all studies that explored attitudes or intentions of sharing HD, which provides a qualitative summary of these findings for all data types, depending on the type of use (primary or secondary).This systematic review adds value by summarizing the findings related to sharing intentions, and those related to underlying motivators, barriers, concerns expressed by the study participants, and significant predictors that were found to modulate and impact their sharing behaviour.

Search strategy and selection criteria
We conducted a comprehensive literature search following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.We searched PubMed, MEDLINE, PsycINFO, Web of Science, EMBASE, and CINAHL for studies published in English between published in English between database inception and February 28, 2023, using a predefined set of keywords.This systematic review is reported following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, and the PRISMA reporting checklist is included as Supplementary materials.The PROSPERO database contains the registered protocol for this systematic review under the registration number CRD42023413822.The necessary amendments and updates were made during the process of conducting this review and can be accessed via PROSPERO.
The researchers created a tailored set of search terms to align with the specific research questions of this study.Boolean operators were employed to optimize the search effectiveness within the chosen databases.Detailed information on the specific search terms used is presented in Supplementary Table S1.These keywords were utilised to retrieve relevant literature from the six databases.
The retrieved articles were imported into the Endnote reference manager software for duplicate removal, after which the final list of articles was imported in Rayyan software, and two investigators (Y.A.A and A.P) independently evaluated the titles and abstracts of the retrieved articles to determine their relevance.Conflicts were resolved either by reaching consensus or by contacting the third reviewer (F.C.).After excluding articles that were deemed irrelevant at this initial stage, full-text versions of the remaining articles were obtained and assessed for eligibility and screened in the same manner.Inclusion criteria for the study encompassed the following: 1) Studies reporting attitudes towards sharing HD, including motivators, barriers, and factors influencing data sharing; 2) Quantitative primary studies, including randomised controlled trials, quasi-RCTs, controlled before-and-after studies, crosssectional studies, interrupted time series, cohort studies, case-control studies, and ecological studies; 3) Mixed-methods primary studies if the qualitative and quantitative sections were presented separately; 4) Qualitative studies reporting quantitative results related to willingness to share HD.The exclusion criteria were defined as follows: 1) Non-human studies; 2) Conference proceedings, studies lacking full-text accessibility, and non-primary studies (e.g., reviews, systematic reviews); 3) Studies that did not report any result of interest; 4) Studies that focused on sharing biospecimen, except when biospecimen were mentioned together with other HD of interest; 5) Studies that focused only on participating in research studies (with the exception of studies that also reported willingness to share HD, such as in biobank studies); 6) Studies that explored sharing behaviours of populations not of interest (such as only healthcare personnel); 7) Studies that explored sharing behaviour only with friends, family, peers, etc.; 8) Studies that were retracted; 9) Studies not written in the English language.
Finally, in instances where a single study or dataset was published in multiple publications, preference was given to the publication that presented relevant outcomes and additional pertinent information aligned with the research question.Additionally, priority was given to publications published in a more suitable format, such as original research articles, over research letters.

Statistical analysis
After the screening stage, a structured table was created to facilitate data extraction.Two reviewers conducted the data extraction process using a predefined table that was reviewed by coauthors.The extracted data encompassed various aspects, such as author information, year of publication, study title, study design, description of the study sample, type of HD involved, duration and recruitment period, specific types of HD collected, purpose of data collection (e.g., primary or secondary use), study objective(s), and key findings-those related to HD sharing attitudes (either in general or depending on type of data/user), those related to barriers/motivators/benefits/concerns of the study participants, and those related to sociodemographic and other variables that were recognized to significantly impact HD sharing behaviour.When a publication reported extensive results both for primary and secondary purposes, this information was extracted and presented in two separate entries (one study was presented twice in two separate rows).In cases when the information for HD sharing attitudes for both primary and secondary uses was not extensive and was presented with the aim to compare them, then the study was presented as one entry, by summarizing both types of findings.Thus, the comparisons depending on the type of use were primarily carried out by comparing the findings from individual studies, except in the case of studies classified as those reporting findings regarding sharing for both types of uses, in which these comparisons were already carried out as that was the study aim.Furthermore, all other findings on the influence of sociodemographic and data-specific factors were presented by summarizing the findings from individual studies depending on the type of data use.
The results were categorized in the following way.First the studies were divided according to the type of data that was shared (Table 1: person/patient-generated health data; Table 2: personal health data/information; Table 3: biobank health data; Table 4: genomic and genetic health data; Table 5: miscellaneous types of data).Further, they were divided (and reported in the Tables 1-5) depending on the purpose the sharing was focused on (primary, secondary, both primary and secondary).The results concerning the rates of willingness to share are presented in a descending way, starting from those that reported the highest rates.After those studies, we presented the studies that reported variability in sharing intentions depending on data type, data user, and similar factors.Finally, at the end we also enlisted the studies that did not report rates of sharing intentions (but only the findings concerning significant influencing variables and concerns/benefits presented in Tables 6 and 7), and presented their summary characteristics, which is in accordance with the PRISMA 2020 checklist requirements.Other results regarding identified concerns and benefits are presented in Table 6, and those concerning significant sociodemographic and data-specific variables are presented in Table 7. and data specific variables are presented in Table 6 and those concerning identified concerns and benefits are presented in Table 7.Here we also need to mention the semantics issue we observed in studies from the second subgroup: personal health information and personal HD were used interchangeably, and the term personal medical information was used in the same context as personal health information.Therefore, we categorized these data as personal HD and information (PHID) in general.We aimed to explore potential differences between electronic vs non-electronic data, thus we made a distinction for all data types whether the data was electronic (when it was specifically mentioned that data sharing was performed electronically, or the data is digital); when no such details were provided, we did not make any specifications.Further, in case of the 2nd subgroup (which includes studies that reported sharing different types of PHID), since the HIE includes technologies that support capturing and sharing of all electronic healthcare information (not only Electronic Health Records (EHRs), 18 we also made a distinction between PHID for HIE, and PHID stored in either EHRs, Electronic Medical Records (EMRs), Personal Health Records (PHRs) or Personally Controlled Health Records (PCHRs).The distinction between EHRs, EMRs, PHRs was also necessary since these records differ between each other in some respects. 24,25When commenting on the findings of the summarized studies, general willingness to share some type of data was presented as such, but when this result was presented depending on the user, type of data, consent or similar, it was used as a factor that impacts the willingness to share.Finally, we should also highlight what different categories of HD use purposes mean: 1) Primary use: those uses directed to patient care, referring to sharing HD with healthcare personnel.2) Secondary use: uses that are directed to purposes other than patient care, such as research, education, public health, often requiring the sharing of HD with universities, pharmaceutical or insurance companies, and more.
To assess the quality of the studies included in this review, the Newcastle-Ottawa Scale (NOS) was utilised.Two investigators independently evaluated the risk of bias, and in cases of disagreement, a third investigator was consulted for resolution.The NOS tool was employed to assess the quality of 116 non-randomised studies.
The NOS serves as an assessment tool specifically designed for evaluating the quality of non-randomised studies and aims to incorporate quality assessments into the interpretation of meta-analytic outcomes.It consists of seven categories for scoring and utilises a star system that is rolled up into three main themes: Selection (maximum of 4 stars across 4 sub-categories), Comparability (maximum of 2 stars across 1 subcategory), and Outcome (maximum of 3 stars across 2 sub-categories).The sub-categories are sample representativeness, sample size, comparability between respondents and non-respondents, ascertainment of exposure, comparability in terms of participant distribution and analyses, assessment of outcome, and statistical tests.For a comprehensive list of adapted questions from the NOS, please refer to the supplementary file provided (Supplementary Table S2).
All graphical presentations are generated by using Microsoft PowerPoint and Microsoft Excel software.To calculate the percentage of willingness to share HD across studies, the percentages across studies are pooled weighted by the number of respondents in each.The graphical presentation concerning willingness to share depending on the type of data and type of data recipient utilised bar charts, and to generate them, we extracted the total number of participants and the total number of those who were willing to share their HD from each study, and calculated the final percentage of those who were willing to share their data by using this formula:

Ethical approval
Ethical approval is not required for this particular systematic review as it relies on data obtained from previously published studies.Each of the studies incorporated in this review had already obtained ethical approval from their respective primary investigators before conducting the data collection.

Study characteristics
The main categorization of the findings from the studies identified in this systematic review was performed based on the type of data they were referring to, of which there are five subgroups.Within each subgroup, the results were then divided according to the type of use and were presented in the following order-first we presented findings related to sharing behaviours for primary uses, then for secondary uses, followed by for both primary and secondary uses.Survey-based studies were present the most (there were a total of 103 of them, and 8 either used vignettes or were defined as experimental), 11 were defined as mixed-method studies, 2 studies used discrete choice experiments.There were no RCTs identified, and only one survey study explored how willingness to share changed after undergraduate students were provided with educational material, which is the only study that explored how some kind of intervention influenced this variable. 41The subgroup of studies for reported behaviours related to the sharing of person-generated HD (PGHD) which represents only data that is generated by the patient (not providers) via home health equipment (such as those for blood glucose or blood pressure monitoring), via mobile device apps, wearable devices, or sensors (n = 17 and 10,771 participants, out of which 15 studies specifically referred to electronic data). 42Studies from the second subgroup reported sharing attitudes and behaviour of information that was described as PHID in general (n = 69 and 117,054 participants, out of which 46 studies referred specifically to electronic data).More details about the semantic issue of this subgroup of studies are provided in the corresponding Methods section.The third subgroup represents the studies that explored sharing intentions of biobank research data (n = 7 studies and 27,073 participants), the fourth focused on genomic data (n = 13 studies and 54,716 participants) and the fifth subgroup collected findings from studies that reported different types of data and were thus described as miscellaneous (n = 10 studies and 18,887 participants, out of which 7 specified referring to electronic type of data in specific).It should be noted that studies that investigated sharing genomic and biobank data did not make specifications on whether the data was electronic, however, as all these studies were published in the last decade, and the repositories are digital, we can presume that they are.The detailed characteristics of studies according to subgroups is given in Supplementary Table S2.

Quality appraisal
The outcomes of the assessment of study quality with NOS are shown in Supplementary Table S3.The assessment results reveal the distribution of study quality based on the provided data.Among the studies evaluated, the majority displayed a moderate level of quality, specifically, there was 1 study rated with 3 stars, 6 studies with 4 stars, 7 studies with 5 stars, 18 studies with 6 stars, 20 studies with 7 stars, 38 studies with 8 stars, 17 studies with 9 stars and 9 studies with the highest score of 10 stars.These findings shed light on the varying levels of quality observed across the included studies and provide valuable insights into the overall rigor and robustness of the research conducted in this field.

Studies reporting intention to share of persongenerated health data Attitudes, behaviour, willingness towards sharing persongenerated health data
Studies focused on sharing person-generated health data (PGHD) for primary purposes reported relatively high willingness (Table 1), the highest being reported from a USA study on pregnant women (93%) that would share wearable device HD with their doctor 43 and the lowest reported by a UK study (74%) that included 250 adults living with a range of long-term health conditions who would be willing to share health and lifestyle data with their healthcare professionals. 44There was a difference in willingness to share this type of data depending on the type of recipient (healthcare providers received more support, 45 even compared with personal social circles) [44][45][46] and the type of PGHD. 46,47mong the studies focusing on sharing PGHD (Table 1) for secondary purposes, the highest rate was reported by an Australian study (77%) on 101 Australian adults regarding sharing health and lifestyle data for research, 48 and the lowest (48%) was for sharing stress data as reported by 29 participants from Sweden and Ireland (however, 72% of the participants expressed their intention to share fitness data). 49One German study also observed lower preferences for sharing with friends, than with scientists, general practitioners, or psychotherapists. 50tudies focused on sharing for primary and secondary purposes (Table 1) revealed that doctors, universities, and public health institutions were viewed more favourably in terms of sharing PGHD compared with private or non-profit companies. 51The quantitative summaries of willingness to share depending on the type of data and on the type of data recipient are given in Fig. 2.
Benefits, concerns, and facilitators found to impact sharing person-generated health data Concerns, benefits, and facilitators perceived by the study participants are presented in Table 6.Privacy was the most reported concern when sharing PGHD for primary uses, 46,52,53 and transparency and privacy was considered significant in the case of sharing for secondary purposes. 43,48  (26), and Germany (13).

Survey-based study
The purpose of this study is to survey and examine factors that may motivate sharing self-collected health data.
Groups do not differ in sharing lifestyle/dietary information, signs of infection, daily mood, geographical location, sleep duration, social environment.
Abbreviations: HD, health data; NR, not reported; AOR, adjusted odds ratio; SD, standard deviation; CI, confidence intervals; M, mean; HCP, healthcare professionals; OR, odds ratio.(3) measure willingness to accept the risks of not sharing information.
Over 95% of patients were willing to share all their medical information with their treating physicians.There was no difference in willingness to share between primary care and specialty sites including psychiatry and an HIV clinic.
Medford About half (49%) of respondents reported that they would be willing to share all their health information with their healthcare provider, less than a third (32%) would share some of it, and about 12% would refuse to share any information.
Studies reporting sharing behavior/attitudes/intentions for primary purposes depending on data user The majority (84%) of individuals were willing to share their PHI with clinicians involved in their care.Fewer individuals (39%) were as willing to share with non-clinical staff.

Survey-based study
The objective of this paper is to establish patient attitudes to ownership of their own medical data and the sharing thereof.
Although 93% of respondents were willing to share data, only 41% were currently doing so and a further 8% did not know whether they were sharing any information in this way.NR/Germany Focus groups were followed by a conjoint-decision study The aim of this study was to investigate acceptance-relevant criteria that people apply to the vision of sharing their medical data on the Internet.
Independently disliked of their age, users disagreed to sharing data regarding mental illnesses, also disliked high identification risks and commercial use of the data but would be willing to share data scientific purposes.

Discrete choice experiment
The aim of this study is to elicit the preferences of the public in different Northern European countries for sharing health information in different contexts.
Respondents in this study indicated that they preferred to share their data when a national authority was going to be the new user of the data.The second preferred new user was an academic research project.
Studies reporting sharing behavior/attitudes/intentions for secondary purposes depending on data type To assess how accurately people understood the effectiveness of techniques for protecting the privacy of shared health data.
There was a big tolerance for researcher use of health data with consistent preference to share data when better privacy-preserving techniques were employed.There was a slight preference for aggregated data over differential privacy, despite differential privacy being objectively more secure.Most reported that they would expect to be explicitly asked for consent before their identifiable record was accessed (91%).However, half (49%), reported that they would not expect to be asked for permission before their de-identified record was accessed.
Abbreviations: HD, health data; NR, not reported; PHID, personal health information and data; PHI, personal health information; SD, standard deviation; AI, artificial intelligence; AOR, adjusted odds ratio; CI, confidence interval; HIE, health information exchange; EHR, electronic health record; PCHR, personally controlled health records; OR, odds ratio; IQR, interquartile range; ICU, intensive care unit; GP, general practitioner; EMR, electronic medical records; ED, emergency department.
Table 2: Summary characteristics of studies reporting findings related to sharing attitudes towards sharing of personal health data/information.
primary purposes such as improvement in delivery and management of health care for them and others, 43,44 while in the case of sharing for secondary purposes common good and better quality results were seen as beneficial. 54ciodemographic and data-specific variables affecting sharing of person-generated health data Among the included studies that reported sharing PGHD for primary and for secondary purposes, younger age was found to be a consistent significant factor that was associated with higher willingness to share. 47,49,53,55,56Other significant sociodemographic factors are listed in Table 7.For this group of data, reliance on mobile devices (including mobile health, smartphones, and wearables) 45,56 was identified as a significant determinant of higher sharing intentions of study participants, among others.
Studies reporting intention to share personal health data and information Attitudes, behaviour, willingness towards sharing personal health data and information Given that almost all of the other subgroups referred to electronic HD (studies reporting sharing of PGHD, PHID in general and biobanking data) and that the fifth category includes miscellaneous data (which disabled us from deriving conclusions also based on whether the data was electronic), this category had enough studies referring to both similar data with specifications about the use of the data (primary, secondary, or both) so we made a distinction in the presentation of results here.Sharing PHID (Table 2) for primary purposes was reported to be highest in the case of USA patients with their treating physicians (95%), 57 and the lowest by another study that included USA public regarding sharing their data with their healthcare providers (49%). 58ne group of studies reported variability in sharing intentions depending on the data recipient, with findings indicating that healthcare providers that are in more direct relationships with the data donor/patient (such as doctors in charge, doctors they were referred to, healthcare providers working at the study sites), received higher support compared with other healthcare staff the participants have not encountered while receiving care.Studies also observed that sharing intentions differed based on the consent scenario, 18 HIE architecture models, 59 whether it was the case of emergency 19,60 and whether healthcare providers implemented and utilised a direct exchange mechanism. 61haring PHID (Table 2) for secondary purposes was found to be highest in a study involving rare disease patients residing in the European Union (97%), 62 while the lowest was found in a study that included USA healthy non-Hispanic white mothers (36%). 63The group of studies that distinguished sharing intentions depending on the data user (n = 14), consistently reported that institutions that conduct non-profit research (such as public hospitals, universities, other academic research institutions), receive higher support than organizations that are involved in for-profit and commercial research (pharmaceutical companies, insurance companies), or (health) government agencies.Willingness to share according to the type of data recipient is presented in Fig. 2. Three studies reported that data type determines one's willingness to share, and one of them observed that participants were in favour of sharing "traditional" health data as opposed to social media and device/app data. 64Other insights provided by these studies suggest that people categorized as belonging to the pandemic cohort were more comfortable with sharing their PHID compared with those belonging to the pre-pandemic cohort, 65 and that privacy-preserving techniques can play an important role in someone's willingness to share. 66,67rom the group of studies (Table 2) that reported how willingness to share for both primary and secondary purposes (n = 13), 12 reported that data recipients involved in their care (primary use of data) received higher support than those involved in secondary data uses (research institutions, pharmaceutical companies, and others).One study observed equal support for both healthcare and research (71%). 68nefits, concerns, and facilitators found to impact sharing personal health data and information Studies reported that study participants perceived sharing HD for primary purposes can bring benefitsthat are mainly directed to improvement of management and delivery of healthcare (including reduction of errors, better accuracy of their records and better communication with their providers).However, they also expressed concerns, and the most frequent ones are those related to privacy and safety of their data.Finally, this review identified that patient involvement in exchanging clinical records 61 and anonymization 69 could increase sharing intentions of HD (Table 6).
Among the studies focused on sharing intentions for secondary uses, the most frequently reported concern was again related to privacy (n = 6), followed by data misuse (n = 2).Study participants perceived that sharing their PHID for secondary purposes will bring benefits in the health and care system, and help both patients and researchers to better understand diseases and health conditions.These findings are presented in detail in Table 6.

Sociodemographic and data-specific variables affecting sharing of personal health data and information
Studies reporting sharing intentions of (electronic) PHID in general for primary purposes found associations with several sociodemographic variables (Table 7).For example, increased willingness was To assess parents' willingness to enroll their children in biobanks, and their perceived benefits, concerns, and information needs under different consent and datasharing scenarios, and to identify factors associated with willingness.
Overall, 55% (95% CIs: 50-59%) of parents were willing to enroll their youngest minor child in a hypothetical biobank; willingness did not differ between consent and data-sharing scenarios.Among all participants in the 2019 survey, 36.7% would be willing to provide information about themselves to a biobank, while 53.5% would not be willing to do so.
(Table 3 continues on next page) associated with achieving higher education, 67,70 someone in a healthcare occupation, 71 female gender, 72 older age, 72 and male gender. 59However, it should be noted that female gender was confirmed to be a significant predictor based on a study involving 20,076 participants, while the other study that found men to be more willing involved a much smaller sample size (n = 170).
Among the studies that focused on sharing behaviours for secondary uses, younger age was found in four studies to be significantly associated with higher sharing intention (n = 4 and involved 112, 222, 8004 and 310 participants respectively), [73][74][75][76] while one study found the opposite (n = 1575). 77Higher education level was confirmed to be a significant predictor of increased willingness to share in two studies (n = 800 and n = 8004) 67,75 while again Buckley et al., concluded the opposite (n = 1575). 77ignificant variables that were identified to be relevant and specific for sharing PHID for primary purposes were related to satisfaction with and trust in healthcare, EHRs, healthcare providers and imposing adequate privacy safeguards.On the other hand, among the many enlisted data-specific variables regarding sharing PHID for secondary purposes, it can be observed that level of data identification, knowledge about data uses, users, and the level of data sensitivity and are very important factors that determine data sharing intentions (Table 7).

Studies reporting intention to share biobank data Attitudes, behaviour, willingness towards sharing biobank data
The willingness of sharing this type of data (Table 3) varied from as low as 10% in Arab countries, 78 to very high of 96% among Canadian participants. 79Only one study reported parents' willingness to enrol their youngest minor child in a hypothetical biobank and reported a rate of 55%. 80nefits, concerns, and facilitators found to impact sharing biobank data Studies reported the same concerns (Table 6) related to sharing biobank data as with the previously discussed data types: trust, privacy, 78,80,81 and the misuse of data, 80 while sharing data with international researchers concerned Arab people. 78However, study participants also perceived that sharing biobank data would lead to improvements in healthcare. 79,82

Sociodemographic and data-specific variables affecting sharing of biobank data
We identified several sociodemographic predictors (Table 7) of higher willingness to participate in biobank research and donate data, of which higher educational attainment and lower religiosity was confirmed in two studies. 80,81 Participants' willingness to donate biospecimens and health data was less than 10%.
Abbreviations: HD, health data; NR, not reported; eMERGE, the Electronic Medical Records and Genomics (network); SpecTRA, Spectrometry in TIA Rapid Assessment (study); SD, standard deviation.Willingness to share genomic data varied from 13.0 (for data being made publicly available) to 91.9% (for sharing with notfor-profit organizations) for anonymous and from 0.9 (for data being made publicly available) to 72.6% (for sharing with universities and research institutes) for identifiable data.Overall, participants were between 12.1 and 31.1% less likely to share their identifiable than anonymous genomic data.

2013/USA Survey-based study
This quantitative study set in the USA examines participant preferences and evaluates differences by demographics and cancer history.
Willingness to share was highest for researchers at the same university and nonprofit organizations, followed by researchers at other universities in the USA, and lowest for any researcher who requests the information and for-profit, private organizations such as pharmaceutical companies. (

NR/Finland Survey-based study
To explore how the provision of educational information relates to willingness to consent, as well as differences in privacy concerns, information sensitivity and the perceived trade-off value.
Of the respondents, 65% were initially willing to consent, but after educational information 89% were willing to consent and only 11% remained unwilling to consent.
Studies reporting sharing behavior/attitudes/intentions for secondary purposes that reported results other than willingness to share Most (64%) declared they would be willing to share their DNA and medical information for use by at least one data user (doctor, non-profit or for-profit userdata not given for each category).
(Table 4 continues on next page) positive attitudes toward biobanks, 78 perceiving more research benefits, fewer concerns, and fewer information needs. 81udies reporting intention to share genomic data Attitudes, behaviour, willingness towards sharing genomic data The one study that reported genomic data sharing (Table 4) for primary purposes found that only around 55% of the USA public would consent to broad data sharing in a clinical setting. 83Willingness to share genomic data for secondary purposes (research) was highest among Norwegian newly discharged hospital patients (90%), 84 and was the lowest among Swiss participants (55%). 85Four studies concluded that for-profit research received the lowest support.The only intervention study identified in this review observed a 24% increase in sharing genomic data for research among undergraduate students after educating them on the benefits. 41Studies that explored sharing genomic data for both primary and secondary purposes (part of the global online survey "Your DNA Your Say") report relatively high willingness among the English-speaking publics from the UK, the USA, Canada, and Australia (67.77%) 86 and Italy (64%). 87Willingness to share according to the type of data recipient is presented in Fig. 2.

Benefits, concerns, and facilitators found to impact sharing genomic data
The perceived benefits from sharing genomic data (Table 6) for secondary purposes were related to research advances and advancing treatments and cures. 85,88On the other hand, data safety, control, access, and the risk of being discriminated were perceived as important concerns. 41,85,88,89The studies that explored sharing for both primary and secondary purposes shared participants were afraid of their "DNA being copied and planted at the scene of a crime", or their family and friends knowing something about them (data not shown). 86ciodemographic and data-specific variables affecting genomic data sharing intention Studies that explored (Table 7) sharing intentions for primary and secondary purposes observed that younger participants expressed a higher willingness to share their genomic data. 86,87,90In the case of sharing genomic data for both primary and secondary purposes, nonreligiosity, 87 White race and higher education level 86 were significantly associated with higher sharing intentions.
For this type of data, familiarity with the concepts of DNA, genetics and genomics and trust in multiple actors, 91,92 having personal experience with genetics and holding genetic exceptionalist views 86  Abbreviations: HD, health data; NR, not reported; IQR, interquartile range; SD, standard deviation; M, mean; EMR, electronic medical records; CI, confidence interval, ED, emergency department.important role in shaping public opinion towards sharing their genetic/genomic data.
Studies reporting intention to share of data categorized as miscellaneous types of data Attitudes, behaviour, willingness towards sharing health data categorized as miscellaneous Studies that reported sharing attitudes of HD as miscellaneous (Table 5) for primary uses found that willingness to share medication records was higher when the recipient was a close relative or their physician (compared with the pharmacy and other health care staff), 93 and that patients with multiple-sclerosis were more willing to share their non-psychiatric HD through their EMRs, compared with psychiatric HD. 94 Studies that focused on sharing attitudes for secondary (research) purposes found the highest willingness to share clinical data among the adult Danish population (90%) 95 and the lowest was observed among social media users, where 71% consented to share their social media data for the purposes of comparing it with their EMR. 96nefits, concerns, and facilitators found to impact sharing miscellaneous types of health data Studies that focused on sharing HD as miscellaneous for primary purposes did not report these outcomes.[98] Sociodemographic and other variables affecting sharing intention of miscellaneous types of health data Interestingly (Table 7), two studies concluded that older age was associated with a higher willingness to share HD-one focused on medication records for primary purposes 93 and another focused on COVID-19 data for secondary purposes. 99Those with higher education levels and a positive COVID-19 vaccination status showed higher support for sharing COVID-19 data for secondary uses. 99Other data-specific factors included societal and self-stigma (in case of sharing nonpsychiatric and psychiatric medication among patients with multiple sclerosis), 94 higher levels of satisfaction with the UK's NHS, personal experience of mental illness (sharing mental health illness data), 100 communication about prosocial benefit or social-life-enabling benefit of the app, and higher perceived risk of the disease (in case of sharing COVID-19 infection data to a tracing app). 98

Discussion
Sharing health data (HD) for primary purposes is a common experience for people to receive the best care.There are several categories of HD that can be part of the process of sharing information with health professionals, healthcare providers and health insurances including PGHD, PHID in general, genomic, and miscellaneous types of data such as medication records and psychiatric information, among the others identified throughout this systematic review.There is also a general need in many sectors of civil society for expanding the use of HD for secondary purposes such as medical research, health policies, public health purposes, national and international statistics, and personalized healthcare.However, the secondary use of HD points to the importance of public acceptance towards data sharing practices. 12This means that secondary use of HD should be aligned with the interests of the public (HD donors), and consequently it must be supported by the general population.
According to the evidence summarized in this review, people are generally very willing to share HD for primary purposes.This confirmation was derived from a sufficient number of studies that reported sharing intentions for PGHD and PHID, while only a single study was identified as relevant for this conclusion for genomic data but confirmed that people are moderately willing to share HD for primary purposes (55% were willing to do so).Biobank data did not apply as that data is oriented towards secondary uses only.Interestingly, several studies consistently found that people are more open to sharing their PGHD with healthcare providers compared with their inner social circle.Further, this group of studies point to the importance of different cofactors that inevitably shape an individual's   willingness to share their data, such as the type of data recipient, the type of consent, type of HIE architecture, or whether it's a case of a health emergency.Although sharing for primary purposes is usually considered as an act that is expected to happen (regardless of any other influences) so the patient can receive the best care, the evidence says the opposite-sharing personal HD within the healthcare system is indeed quite complex.The evidence concerning sharing data for secondary purposes is significantly different from the primary ones: it is clear the willingness to share is higher for primary than for secondary purposes and the difference mainly concerns specific data types.This is evident when inspecting the studies that assessed sharing intentions for both primary and secondary uses in the case of PHID-80% of studies concluded higher support in cases of sharing for primary uses.However, studies on sharing PGHD for secondary purposes report variable and often opposite findings among the general population, highlighting the need for more investigations.On the other hand, the number of studies on sharing PHID (electronic and non-electronic) that report higher rates of willingness to share for secondary purposes significantly outweigh those that report lower rates (75% vs 25%).It is interesting that both the highest and the lowest sharing rates for secondary purposes (among all studies) were observed in the case of biobanking data (98% and 10%, respectively).Sharing genomic data received relatively high support (65%) from two studiesone involving populations from several countries and one involving Italian general population (part of the project "Your DNA Your Say").
Again, people's willingness to share their data for secondary uses is shaped by different factors, such as the type of consent model, the type of privacy-protection tools, the state of emergency (e.g., the COVID-19 pandemic), the type of data use, and what is most important the type of data user.Consistently, across all data types, non-profit organizations, and public institutions (universities, research institutes) received higher support than those that use data for making profit or are private (e.g., pharmaceutical companies).As expected, education and knowledge about data sharing is of great importance, however, there was only one intervention study involving Finnish undergraduate students, in which the rate of supportive students increased significantly after receiving education about sharing genomic data for secondary purposes.Thus more intervention studies are needed to confirm this suggestion.
The opinions of study participants that reflect their concerns or perceived benefits regarding sharing their data, can either decrease or increase their intention to share.The comparison analyses of all studies revealed several mutual perceived concerns for all types of data (such as privacy and security) and data misuse (Fig. 3).On the other hand, perceived benefits that were mutual for all data types were related to improvement in healthcare (in specific aspects and in general).Evidence suggests that people need transparency and reliable

Person-generated health data
Concerns: privacy, security, usefulness of their data, transparency, cost (of the devices)  privacy and security protection tools in order for them to be more open towards sharing their personal information.This qualitative summary also identified what important sociodemographic factors determine willingness to share HD.In the case of sharing PGHD, younger age was consistently reported in several studies to significantly affect sharing this type of data for primary uses.Evidence from studies that reported sharing PHID for primary purposes found conflicting findings regarding the association between gender, education level and age.In the case of gender, the findings that suggest women are more open to sharing PHID was derived from a much bigger study sample compared with the other study suggesting the opposite (20,076 vs 170, respectively), indicating that female gender might be a more reliable predictor based on these two studies.Further, it can be suggested that both younger age and higher education level can be considered a more reliable predictor (than their counterparts), since they are derived from a higher number of studies (and consequently are based on a higher number of study participants).However, deriving such an accurate conclusion certainly requires a quantitative synthesis involving a meta-analysis as the most appropriate solution.Interestingly, younger age, higher educational attainment and being less religious were mutual sociodemographic predictors for sharing both biobank and genomic data.However, some of these insights were derived from individual studies, and thus these findings would benefit from additional studies that will confirm them.
Finally, the variables that were found to be specific for each data type should not be neglected.Such information can be used as information when addressing the needs of specific populations that were found to be more reluctant to share their data.For example, it was evident that people that are more reliant on mobile devices and smartphones are also more open to PGHD data sharing, compared with those who are not reliant.People who are not familiar with the concepts of genetics are also more hesitant to share their genomic data.As a matter of fact, only one intervention study was identified in this systematic review, and the evidence suggests that this research field would benefit from more RCTs (or similar intervention studies), that design specific interventions that can be used to address the concerns and barriers that people tend to have regarding sharing their data and potentially increase their willingness to share.
Sharing data is shaped by different ethical dimensions and can pose both positive and negative consequences.Findings from this qualitative synthesis confirm that the ethical aspect of data sharing is widely present in the opinion of data donors, and they perceive that sharing their data will bring benefits at the healthcare or the scientific level.Although people do acknowledge these benefits, they still lack understanding of different ethical aspects that relate to sharing practices.For example, it was seen from some studies that people would restrict the access to their data to specific parties (researchers that were in charge) or to specific uses (already defined in the protocol).However, many times the need for using their data may require different users and uses that are aligned with the final goal of reaching a better health-related outcome.All these details need to be presented in appropriate consent forms and institutional review board protocols 101 and should be in accordance with the needs of both data donors and users.Thus, to ensure trust from data donors about sharing practices, they also need to be educated about what ethical data sharing involves.
On the other hand, there are ethical aspects concerning data donors that must be addressed in the data sharing process (which are mainly perceived as concerns-privacy, security, stigma, discrimination, among others).For example, based on the collected evidence, it is evident that privacy and security remain the most expressed concerns of data donors.Imposing adequate privacy-protecting tools (such as deidentification and anonymization) is found to alleviate these concerns (instead of asking participants to consent to using their identified data) which can bring positive consequences to data users (such as reduced research cost, increased recruitment rates, and decreased recruitment bias). 102n the other hand, it can bring considerable harm to the data donor in case their identity is revealed.It was evident throughout this review that transparency was an important concern and was a need that the participants expressed.Together with enforcing reliable protections, the public should also be informed about these activities and the corresponding consequences and benefits.
In line with the observations derived from this qualitative synthesis, a group of researchers from the University of Manchester found a high rate of people opting out of data sharing program after the government announcement of General Practice Data for Planning and Research (GPDPR) program in 2021, because people object to the patient-oriented constraints introduced by the GPDPR proposal (for example, patients are not allowed to decide whether their data should be shared, about the data recipient and the purpose of use 103 ).There is a clear need for new regulatory approaches that will address the public's lack of trust in institutions, organizations, and the process of data sharing, that will enable higher transparency and find a more patient-centric solution that will provide them with more control without compromising the goals of HD use.
However, to fulfil its value, data sharing is suggested to follow the Findable, Accessible, Interoperable, Reusable (FAIR) principles. 104The problem with adhering to these principles occurs because data lacks standardization from the beginning.There is a clear need for exchange standards, domain-relevant content standards and accessible rich metadata, that will facilitate the interoperability in data sharing process, and consequently adherence to the proposed FAIR principles. 104part from those, there are challenges that need to be addressed, including technical, economic, legal, and political barriers.Datasets are constantly growing in their size and complexity, and are followed by incompleteness of data, lack of metadata and standards, lack of interoperability, among others.These issues are further amplified with the fact that different data types and their sources are often combined, in the necessity to overcome these challenges, which makes an even bigger problem because data sharing mechanisms are often data type specific. 105Robust standards and global collaborative efforts are necessary to address the barriers that hinder interoperability and the data sharing process.
On the lower level, data sharing strategists should consider potential barriers and challenges upfront, identify and then address them (before launching the final data sharing model).Some of them are enlisted in this review and can be utilised in defining the approach for addressing the needs of patients/public as stakeholders.This can be done by designing a practical minor intervention that will justify the value of the data-sharing program to the involved stakeholders (including professionals that act as data users and patients as data donors). 106Giving them knowledge and the (practical) implication about the benefits of their engagement in the data sharing process can ensure a better data sharing culture and motivation among different stakeholders.
Finally, we are going to point to strengths and limitations of this systematic review.Firstly, this review provides the first very detailed and thorough qualitative synthesis related to the issue of HD sharing attitudes that included all HD types identified in the published studies.It is the first to provide a comparison synthesis by making a distinction between primary and secondary uses and to make comparisons and conclusions for other variables and aspects that were found to significantly shape the will of people.It can be used as a map of available scientific evidence, which segments can be applied in designing both future (intervention) studies and policies that aim to tackle hesitancy and reluctance towards sharing HD where there is an identified need for this.Given its wide inclusion criteria, the limitations of the synthetized evidence are expected.Firstly, the dominance of the studies that investigated sharing of (electronic) PHID is evident, and their number significantly outnumbers studies that investigated other types of data; therefore, future studies should take this into consideration.Further, the evidence synthetized in this form does not allow us to derive straightforward conclusions regarding the differences in sharing intentions based on the type of study population, gender, age, education level.It provides an overview of this evidence, however, an analysis focusing on specific aspects with the attempts to quantify the potential associations is necessary for making straightforward conclusions.Another limitation of this data is also the inhomogeneity of their presentation (some reported odd ratios, other test statistics depending on the difference in the percentages, etc), and this was another hindrance in making accurate and reliable conclusions based on their summaries.
In conclusion, an individual's willingness to share health information can be viewed as a result of weighing both positive and negative factors that are related to the process of sharing personal health information.Positive or negative preferences and attitudes are significantly driven by different factors 107 such as types of health data or information, privacy concerns, information on security level, results of health information use, altruism, illness histories and others.Tools such as gaining consent, data anonymization, and establishing regulations for gaining access to data could facilitate and encourage data sharing.All these factors shape what we call the culture of health data sharing.6][17] Possibly, this change can be driven better by an improved culture and education allowing a safe and promising development of scientific, commercial, and public health purposes.This should also inspire policy makers in requiring guidelines and data regulations-which are now missing-that promote a well-informed, evidence-based and transparent public communication on health data uses, which is fundamental to create awareness and trust in health data sharing for beneficial purposes, further improving our healthcare system and outcomes.

Fig. 2 :
Fig. 2: Quantitative summaries of willingness to share based on data type and recipient.

Fig. 3 :
Fig. 3: Comparative analysis of perceived concerns across all data types and misuse.
Study participants perceived they would gain benefits from sharing PGHD for NR (Table 1 continues on next page)

Table 1 :
Summary characteristics of studies reporting findings related to sharing behavior of person/patient-generated health data.
Table 2 continues on next page) Other significant data-specific predictors included: no previous involvement in research and

Table 3 :
Summary characteristics of studies reporting findings related to attitudes towards sharing of biobank health data.Studies reporting sharing behavior/attitudes/intentions for secondary purposes depending on the type of data user Table 4 continues on next page)

Table 4 :
seem to play an Summary characteristics of studies reporting findings related to attitudes towards sharing of genomic and genetic health data.

Table 5 :
Summary characteristics of studies reporting findings related to attitudes towards sharing data categorized as miscellaneous types of data.

Table 6 :
Benefits, concerns, and facilitators that were found to impact sharing behavior.Higher health self-efficacy, higher level of trust in providers as a source of health information, higher level of physical activity, those with a higher frequency of wearable use, and who reported use of smartphones or tablets to help communicate with providers had greater odds of willingness to share data with providers(Rising et al., 2021).

Table 7 :
Main socio-demographic and data-type specific factors that affect willingness to share.
: improvement their own health and of others, and decrease disease risks